Method and apparatus for verifying uniform flow of a fluid sample through a flow cell and distribution on a slide

ABSTRACT

Particles in a fluid are counted as the fluid flows through an examination area of a particle analyzer. Parameters based upon the particle count are calculated. It is determined whether the at least one calculated parameter is within an allowable range for the parameter. Based on this determination, an indication is provided of uniformity of the fluid sample in the examination area.

This is a Division of U.S. application Ser. No. 09/632,091, filed Aug.2, 2000 now U.S. Pat. No. 6,424,415 which is a Divisional of applicationSer. No. 08/852,519, filed May 7, 1997, now U.S. Pat. No. 6,184,978which claims priority from Provisional Application Ser. No. 60/017,747,filed on May 15, 1996.

TECHNICAL FIELD

The present invention relates to fluid samples in flow cells anddistributed on slides and, in particular, to a method for verifying thata fluid sample is flowing uniformly through the flow cell and isuniformly distributed on the slide.

BACKGROUND

A conventional flow cell and particle analyzer is shown, in plan view,in FIG. 1, and a particle analyzer is shown in block schematic form inFIG. 2. The flow cell and particle analyzer automate the job of countingfluid particles suspended in a biological sample (for example, bloodcells suspended in a serum sample or particles suspended in a urinesample). More generally, the flow cell and particle analyzer may be usedto automate the job of counting any “particles of interest” that aresuspended in a fluid sample.

Referring now to FIG. 1, the flow cell includes a body 10 containing aflow chamber having an inlet 12 through which the fluid sample isprovided. The flow chamber also has an outlet 14. A passageway 16extends between the inlet 12 and the outlet 14 such that the fluidsample provided at the inlet 12 flows past an examination area 18 (shownby a dashed line in FIG. 1) and is discharged out of the passageway 16via the outlet 14.

During a sampling run, the fluid sample is surrounded on both sides bysheath fluid (e.g., saline solution) introduced into the passageway 16from inlets 13 a and 13 b. A strobe light 32 (FIG. 2) illuminates theexamination area 180 from below and a microscope 30 is focused on theexamination area 18 from above. A CCD camera 34 receives the output ofthe microscope 30 to form a series of still frame images.

In particular, a frame grabber 40 “grabs” the still frame images andstores them in a memory. A CPU 56 combines selected components of thestill frame images to generate composite images which represent theparticles in multiple images. Thus, individual particles (for example,different types of blood cells) can be analyzed and sorted.

In order for the particle analyzer to perform reliably, the fluid samplehaving the suspended particles of interest must flow uniformly throughthe flow cell examination area 18, without interruption. Even ifinterruptions occur relatively infrequently, when they do occur theywill cause the determined particle counts to be inaccurate.

SUMMARY

In accordance with one embodiment of the invention, a particle analyzerverifies the uniformity of fluid flow through a flow cell examinationarea without reference to particles of interest (that may or may not besuspended therein). First, the particle analyzer inspects still frameimages generated by a frame grabber of the flow cell and the light levelin the examination area is determined. Then, a heavy stain is injectedinto the flow cell. Thereafter, the particle analyzer inspects furtherstill frame images generated by the frame grabber of the flow cell anddetermines from the further images whether there is a uniformlydecreased intensity in light level in the examination area(corresponding to the heavy stain flowing through the examination area).If so, then the flow of fluid through the flow cell examination area isindicated as being uniform. Otherwise, the flow of fluid through theflow cell examination area is indicated as being non-uniform.

In accordance with another embodiment of the invention, a particleanalyzer executes statistical tests on counts of particles of interestin the fluid sample under analysis and uses these counts to determinewhether the fluid sample is flowing uniformly past an examination areaof the fluid particle analyzer. In particular, statistical parameters ofthe particle counts are calculated. Then, it is determined whether thecalculated statistical parameters are within an allowable range for theparameters. Based on this determination, an indication is provided ofwhether the fluid sample is flowing uniformly past the examination area.

A better understanding of the features and advantages of the inventionwill be obtained by reference to the following detailed description andaccompanying drawings which set forth an illustrative embodiment inwhich the principles of the invention are utilized.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a plan view of a flow cell for moving a fluid sample past anexamination area.

FIG. 2 is a schematic block diagram of a particle analyzer for use withthe flow cell of FIG. 1 in practicing the present invention.

FIG. 3 is a flowchart which illustrates a process in accordance with afirst embodiment of the invention for determining whether a fluid sampleis flowing uniformly past an examination area of the apparatus of FIG.1.

FIG. 4 is a top view of the examination area of the flow cell of FIG. 1.

FIG. 5 is a flowchart which illustrates a process in accordance with asecond embodiment of the invention for determining whether fluid isflowing uniformly past the examination area of the apparatus of FIG. 1.

FIG. 6 is a bar graph which illustrates how the determining step 512 maybe carried out in the process illustrated in FIG. 5.

FIG. 7 is a flowchart which illustrates a process in accordance with athird embodiment of the invention for determining whether fluid isflowing smoothly past the examination area of the apparatus of FIG. 1.

FIG. 8 is a bar graph which illustrates how the determination step maybe carried out in the process illustrated in FIG. 7.

FIG. 9 is a flowchart which illustrates a process in accordance with afourth embodiment of the invention for determining whether fluid isflowing smoothly past the examination area of the apparatus of FIG. 1.

FIG. 10 is a illustrates the data flow of the process shown in theflowchart of FIG. 9.

FIGS. 11-16 are examples of membership functions for computing a degreeof support for various measurement values in step 904 of the flowchartof FIG. 9.

FIG. 17 is an example of a membership function for computing adefuzzified value, FD(Center of Maxes), in step 908 of the flowchart ofFIG. 9.

DETAILED DESCRIPTION

A first embodiment of the present invention is now described withreference to the flow chart in FIG. 3. The steps illustrated in theflowchart of FIG. 3 are executed by the processor 36 of the flowanalyzer shown in FIG. 2. At step 301, when it is desired to determinewhether fluid is flowing uniformly past an examination area of a flowcell (such as the examination area 18 of the flow cell shown in FIG. 1),the processor determines an average light level intensity of theexamination area (or of a portion of the examination area). Preferably,the average light intensity is measured by averaging the intensity ofall portions, of more than one still frame image, that correspond to theexamination area. However, while not as reliable, it is within the scopeof the invention to determine the light level intensity by consideringonly a representative portion of each still frame image (or even of onlyone still frame image). Then, at step 302, a heavy stain is caused to beinjected into the flow cell. For example, the heavy stain may beprovided at the inlet 12 of the FIG. 1 flow cell. The heavy stain may beprovided concurrently with a fluid sample under analysis, provided thatthe stain is not so opaque that it interferes with the ability of theparticle analyzer to distinguish particles in the fluid sample underanalysis. Alternately, the heavy stain may be provided exclusive of afluid sample under analysis (e.g., at the end of a sample run).

After injecting the stain into the flow cell, at step 303, the particleanalyzer looks for a period of uniformly decreased light level intensityin the still frame images, as measured in the direction perpendicular tothe fluid flow. At step 304, if the uniformly decreased light levelintensity was expected to be seen within a first time period sinceinjection of the stain into the flow cell, and the first time period haspassed, then, at step 306, it is reported that fluid is not flowinguniformly past the examination area 18 of the flow cell.

Otherwise, if the period of uniformly decreased light level intensity isseen within the first time period, then, at step 312, it is reportedthat the fluid is flowing uniformly past the examination area 18 of theflow cell.

In accordance with a second embodiment of the invention, the examinationarea 18 is logically divided into sub-areas 18 a through 18 f,perpendicular to the direction of fluid flow, as shown in FIG. 4. Thedirection of fluid flow is shown in FIG. 4 by an arrow A. FIG. 5illustrates the flow of the basic process executed by the processor 36in accordance with the second embodiment of the invention. At step 502,the processor 36 initializes an index variable “i”. Then, at eachexecution of step 504, for the current frame grabbed by the framegrabber 40, the processor 36 determines a count of the number ofparticles “of interest” in each sub-area 18 a through 18 f of theexamination area 18 and accumulates this count for all grabbed framesthus far processed. (The particles that are “of interest” are theparticles that the particle analyzer is designed to detect and analyze.)For example, if the particle analyzer is a blood cell analyzer, theparticles “of interest” may be red blood cells. Alternately, the“particles of interest” may be calibrator particles which have beenadded to a fluid sample for volume calibration of the particles that theparticle analyzer is designed to detect and analyze.

The processor 36 increments the index variable “i” at step 506. Once apredetermined number of frames have been processed, as indicated by “i”being equal to “n” at step 508, the processor 36 proceeds (at step 510)to calculate a statistical distribution of the number of particlescounted in the sub-areas 18 a through 18 f. Then, at step 512, theprocessor 36 determines from the calculated statistical distribution thefluid flow uniformity within the examination area 18.

FIG. 6 is a bar graph whose bars 20 a through 20 f represent theaccumulated counts of the number of the particles in the sub-areas 18 athrough 18 f. The curve 22 in FIG. 6 represents an expected distributionof particles in the sub-areas 18 a through 18 f if fluid is flowinguniformly past the examination area 18. Generally, a uniformdistribution (or some other expected distribution determined by fluiddynamics) of particles is expected. If the distribution of accumulatedcounts as represented by bars 20 a through 20 f in FIG. 6 is within anallowable range of the expected distribution as represented by curve 22,then the processor 36 determines that the fluid is flowing uniformlypast the examination area 18. On the other hand, if the distribution ofaccumulated counts is outside the allowable range, then the processor 36determines that fluid is not flowing uniformly past the examination area18.

In accordance with a third embodiment of the invention, counts ofparticles of interest are accumulated during specified time periods, andthe processor 36 calculates statistical parameters of the accumulatedcounts. Thus, it can be seen that the third embodiment of the inventionis “time-oriented”, in contrast to the second embodiment of theinvention which is “space-oriented”. FIG. 7 illustrates the flow of thebasic process executed by the processor 36 in accordance with the thirdembodiment of the invention. At step 702, the processor 36 initializesan index variable “i”. Then, for each group of “1” frames (e.g., 50frames, where each frame represents sampling at different times) grabbedby the frame grabber 40, the processor 36 sets a count variable, indexedby “i”, to the number of sample particles counted in those frames (step704). The processor 36 increments the index variable “i” at step 706.Once the sample particles have been counted in each of “n” groups of “1”frames (as determined at step 708), the processor 36 proceeds (at step710) to calculate a statistical distribution of the number of sampleparticles counted in the groups of “1” frames. Then, at step 712, theprocessor 36 determines from the calculated statistical distribution iffluid has flown uniformly past the examination area 18 during the samplerun.

A first aspect of the third embodiment of the invention is now discussedwith reference to the bargraph shown in FIG. 8. FIG. 8 shows a bargraphof an example of sample particle counts for each of “n” (in the exampleof FIG. 8, n=11) groups, G₁, G₂, . . . , G_(n) of “1” frames (e.g., 50frames). The height of each bar m₁, m₂, . . . , m_(n) indicates thenumber of sample particles counted for the corresponding group of “1”frames. The dotted line labelled m_(ave) indicates an overall meannumber of sample particles, calculated by the processor 36 at step 610,for each group of “1” frames over the “n” groups. Also at step 710, theprocessor 36 determines, for each group of “1” frames, how far theoverall mean number of particles, m_(ave), per group of “1” framesdiffers from the actual count, m_(j), of sample particles in each groupof “1” frames. This difference is shown graphically in FIG. 8 by a hatchpattern. Then, at step 712, the processor determines if the differencesare within an allowable range. If the differences are within theallowable range, the processor 36 indicates that the fluid sample isflowing uniformly past the examination area 18.

In accordance with a second aspect of the third embodiment of theinvention, the processor 36 calculates, at step 710, a parameter

 x=Σi*m _(i) , i=1 TO n  (1)

assuming that x defines a weighted sum of Poisson random variables.Then, at step 712, the processor 36 determines if “x” is within anallowable range.

In accordance with a third aspect of the third embodiment of theinvention, at step 710, the processor 36 calculates a parameter$\begin{matrix}{{x = \frac{\sum m_{i}}{n}},{i = {1\quad {TO}\quad N}}} & (2)\end{matrix}$

where x is the average number of particles counted in each group of “1”frames”. The processor 36 then calculates a parameter

y=Σ|m _(i) −x|, i=1 TO N  (3)

Since |m_(i)−x| indicates how much the particle count for an individualgroup of “1” frames deviates from the overall average, y indicates thesum of the deviations for the individual groups. Then, at step 712, theprocessor 36 determines if the parameter y is within some allowablerange. The parameter y being within the allowable range indicates,within some degree of statistical certainty, that the fluid sample isflowing uniformly past the examination area 18.

In accordance with still further aspects according to the thirdembodiment of the invention, the examination area 18 is logicallydivided by the processor 36 into the sub-examination areas 18 a to 18 f(FIG. 4). The processor 36 executes the process shown in steps 702-712of the FIG. 7 flowchart for each sub-examination area 18 a to 18 f.

For example, in accordance with a fourth aspect of the third embodiment,at step 710, the processor 36 calculates the statistical parameter x, asshown above in equation (1), for each of the areas 18 a to 18 f. Then,at step 712, the processor 36 determines whether the x calculated foreach area 18 a to 18 f is within an allowable range. In accordance witha fifth aspect of the third embodiment, at step 710, the processor 35calculates the statistical parameters x and y, shown above in equations(2) and (3), for each of the areas 18 a to 18 f. Then, at step 712, theprocessor 36 determines whether the y calculated for each area 18 a to18 f is within an allowable range.

Alternately, and particularly for situations where processing power isat a premium, at step 710, the processor 36 calculates the statisticalparameters from counts of particles in only one or more selected ones ofthe areas 18 a to 18 f. At step 712, the processor 36 determines whetherthe calculated statistical parameters, for the selected areas, arewithin an allowable range. It is noted that this embodiment may not beas reliable as embodiments which utilize the entire examination area 18.

In accordance with a further embodiment of the invention, values areobtained of several measurements, where each measurement is at leastpartially indicative of the flow of the fluid sample through the flowcell. Then, these partial indications are combined to generate a “crisp”indication of the fluid flow. The combining is preferably implemented bythe CPU 56 (FIG. 2) executing special purpose software or firmware.

One relatively simple way to combine the partial indications is to use alinear discriminant function, in which the partial indications are eachmultiplied by a weighting factor (indicative of the contribution of themeasurement) to generate a weighted partial indication. The weightedpartial indications are summed together and compared to a threshold toproduce the “crisp” indication of the fluid flow.

Another way to combine the partial indications is to use “fuzzy logic”.Such a fuzzy logic method is advantageous for use with an automatedparticle analyzer, which is normally unattended. The method preferablyimplemented by the CPU 56 (FIG. 2) executing special purpose software.

FIG. 9 is a flowchart illustrating an embodiment of the fuzzy logicdetection method. At step 902, the fuzzy logic detection method obtainsvalues of the several measurements (measurement values) that are atleast partially indicative of the flow of the fluid sample through theexamination area of the flow cell. At step 904, for each of degrees offlow 1, 2 and 3 (e.g., clog, partial clog and no clog), a degree ofsupport for each measurement value is determined from a membershipfunction of the measurement. At step 906, values indicating the degreeof support are provided to inference machines or rule functions, whichcalculate “fuzzy” degree of flow disruption values. Then, at step 908,calculated degree of flow disruption values are defuzzified to a “crisp”number to determine whether a flow disruption has occurred.

The following terms, defined below, are employed in describing the fuzzylogic detection method in accordance with an embodiment of theinvention.

linguistic variable or measurement

a linguistic description, parameter or metric related to an event orcontrol input (e.g. temperature)

terms

ranked linguistic descriptions of a measurement. E.g., temperature {low,normal, high}

degree of support

degree to which a measurement is a member of the term; normalized to anumber between 0 and 1

membership function

a function that yields its degree of support given a measurement value

inference machine

rules of the fuzzy logic

defuzzification

a process of converting “fuzzy” values to a “crisp” and real value

The fuzzy logic embodiment is now discussed in more detail withreference to the data flow diagram shown in FIG. 10. First, for each ofone or more degrees of flow (e.g., “no clog”, “partial clog” and “fullclog”—shown as degree of flow 1, 2 and 3 in FIG. 10) of the fluid in theflow cell, a degree of support value for each measurement value iscomputed from the membership function for the measurement (step 904). Todetermine the membership function for a measurement, a number of sampleruns are carried out and measurement values determined. Meanwhile, anoperator observes the runs and determines for each run which of the oneor more degrees of flow occurred. Then, the determinations of whichdegree of flow occurred are correlated to the measurement values todetermine the membership function for each measurement. That is, amembership function for a particular measurement provides a numericindicator of association of each of one or more linguistic descriptionsof flow occurrence (e.g., no clog, partial clog and full clog) with aparticular measurement value.

Next, the computed degree-of-support values for each degree-of-flow areprovided to an inference machine for that degree of flow. The inferencemachine calculates degree-of-flow disruption “fuzzy” values (step 906).For example, the inference machine may calculate “fuzzy” valuesCLOG[low], CLOG[norm] and CLOG[high] for each degree of flow where eachrepresents a numeric indicator of an association (low association,normal association, and high association, respectively) that theparticular degree of flow is the actual degree of flow.

Next, for each degree of flow, the “fuzzy values” CLOG[low], CLOG[norm]and CLOG[high] are “defuzzified” into a crisp prediction of whether thefluid sample is flowing through the flow cell with that degree of flow(step 908).

An example of the parameters, the terms of measurements and thegraphical representation of the membership functions are now described.The “critical” points of each term are determined statistically, inadvance, using the mean and standard deviation, and it is the criticalpoints that determine the membership function for the parameter.

For example, approximately 1000 observed samples may be classified intothree groups: no-clog, some-clog and full-clog. The mean and standarddeviation of the measurements for each group of samples are thencalculated. Then, either one or two standard deviations are added todetermine a range of each term.

Stain Measurement (ST)

When it is desired to determine whether the flow cell is clogged (e.g.,at the end of a particular specimen analysis run), additional stain isinjected into the flow cell. Then, the light intensity of subsequentlygrabbed still frame images is measured. If the flow cell is completelyclogged, there will be no discernable difference in light reading beforeand after the additional stain is injected into the flow cell. Thus, themeasured intensity is then translated into a numerical measure ofassociation of whether the flow cell is clogged (true indicating agreater association of the numeric value with the condition that theflow cell is clogged and false indicating a greater association with thecondition that the flow cell is not clogged).

Temporal Flow Variance (TFV)

The TFV measures the variance of the flow rate from frame to frame, orfrom group of frames to group of frames. For example, in one embodiment,the frames are grouped into about 10 bins and the number of analytes ineach bin are counted. These counts/bins are then used to calculateTnorm, a Poisson distribution based parameter. If there are a sufficientnumber of analytes in each frame, then the TFV can be calculated on anindividual frame basis. Example set measurement functions for TFV areshown in FIG. 11.

Spatial Flow Variance (SFV) Measurement

The SFV provides an indication of a variance of distribution ofparticles of interest in a direction perpendicular to the direction offluid flow in the flow cell. As described above with respect to FIG. 4,the examination area is equally divided into a number of sub-areas (18 athrough 18 f in FIG. 4) and the particles of interest (analytes) in eachsub-area are accumulated. Then, these accumulated counts are used tocalculate Tnorm, a Poisson distribution based parameter. As with TFVdiscussed above, if there are a sufficient number of analytes in eachsub-area of each frame, then the SFV can be calculated on an individualframe basis. Example set measurement functions for SFV are shown in FIG.12.

Spatial Delta(max−min) Variance (SDV) Measurement

The SDV also provides an indication of a variance of distribution ofparticles along a direction perpendicular to the direction of fluid flowin the flow cell. The SDV, however, is more sensitive to sharp peaks andvalleys in this distribution than is the SFV. The accumulated counts foreach sub-area are used to calculate delta=(maximum−minimum)/average. Aswith TFV and SFV discussed above, if there are a sufficient number ofanalytes in each sub-area of each frame, then the SDV can be calculatedon an individual frame basis. Example set measurement functions for SDVare shown in FIG. 13.

Concentration Ratio of LPF to HPF (CR) Measurement

The CR measures the difference in concentration of particles of interestbetween the low power field and high power field. The concentration ofthe small particles detected in low power field, designated as sizegroup 4 (LPF4) is divided by the concentration sum of particles in highpower size groups 1, 2, 3, and 4, thus excluding the smallest high powergroup, designated as high power field 5. Theoretically, this valueshould be about 16. The CR gives an indication of whether a clogoccurred between frames grabbed in low power mode of the frame grabber40 and frames grabbed in high power mode of the frame grabber 40.Example set membership functions for CR are shown in FIG. 14.

LPF4 (<EPI class) Concentration (LPC) Measurement

It was empirically determined and reasonable that a high concentrationof small particles (e.g., LPF4 class analytes) is associated with ahigher probability of the flow cell becoming clogged. Example setmeasurement functions for LPC are shown in FIG. 15.

Center of Mass Shift Variance (CMS) Measurement

The CMS measures the temporal shift of analytes along the directionperpendicular to fluid flow in the flow cell. The “center of mass” inthe direction perpendicular to fluid flow for each sub-area 18 a through18 f is calculated. Theoretically, the CMS measurement should be about127.5 if fluid is flowing perfectly uniformly through the flow cell. Thecalculated center of mass/frames is then used to calculate Tnorm, aPoisson distribution based parameter.

Center of Mass=(X[1]+X[2]+ . . . X[n])./n,

where n is equal to the number of analytes and X[i] is equal tox-position of i-th analyte. Again, if there are a sufficient number ofanalytes in each sub-area of each frame, then the CM can be calculatedon an individual frame basis.

Example set measurement functions for CMS are shown in FIG. 16.

An embodiment of the inference machines is now described.

Full Clog Inference Machine

The measurements provided to the Full Clog Inference Machines are ST,TFV, SFV and SDV.

Step 1: Apply Rules (Inference Rules)

CLOG[low]=(ST[false]+TFV[low]+SFV[low]+SDV[low])/4

CLOG[norm]=min (TFV[norm], SFV[norm], SDV[norm])

CLOG[high]=(ST[true]+TFV[high]+SFV[high]+SDV[high])/4

Step 2: Calculate Final Score (Defuzzification) See FIG. 17 for anexample of a membership function for the defuzzified value, FD(Center ofMaxes)

FD(low)=−CLOG[low]*100

FD(high)=CLOG[high]*100

if (CLOG[low]<CLOG[high])

FD(norm)=−(CLOG[norm]*50)+50

else

FD(norm)=(CLOG[norm]*50)−50

WeightedSum=(FD(low)*CLOG[low])+(FD(norm)*CLOG[norm])+(FD(high)*CLOG[high])

FD(Center of Maxes)=Weighted Sum/(CLOG[low]+CLOG[norm]+CLOG[high])

The range of FD(Center of Maxes) is from −100 to 100.

if (FD(Center of Maxes) >20)→Full Clog

Partial Clog Inference Machine

The measurements used in determining Partial Clog are LPC, CMS, CR, TFV,SFV and SDV.

Step 1: Apply Rules (Inference Rules)

Shift Variance (low)=(LPC[low]+CMS[low])/2

Flow Variance (low)=max (CR[norm], TFV[low], SFV[low], SDV[low])

CLOG[low]=(Shift Variance(low)+Flow Variance(low))/2

Shift Variance(norm)=(LPC[norm]+CMS[norm])/2

Flow Variance (norm)=max (TFV[norm], SFV[norm], SDV[norm])

CLOG[norm]=(Shift Variance(norm)+Flow Variance(norm))/2

Shift Variance(high)=(LPC[high]+CMS[high])/2

Flow Variance(high)=max (max(CR[low], CR[high]), TFV[high], SFV[high],SDV[high])

CLOG[high]=(Shift Variance(high)+Flow Variance(high))/2

Step 2: Calculate Final Score (Defuzzification). Defuzzification is donethe same way as the Full Clog fuzzy logic.

if (Center of Max >=25)=> Partial Clog

EXAMPLE

CR=5.02, ST false, LPC=69.5, CMS=7.6, TFV=3.8, SFV=8.02, SDV=1.94

Step 1: Find Degree of Support frm Each Measurement ST[false] = 0.7ST[true] = 0.0 CR[low] = 0.0 CR[norm] = 0.155 CR[high] = 0.0 LPC[low] =0.0 LPC[norm] = 0.0 LPC[high] = 1.0 CMS[low] = 0.0 CMS[norm] = 0.0CMS[high] = 0.326 TFV[low] = 0.0 TFV[norm] = 0.823 TFV[high] = 0.0SFV[low] = 0.0 SFV[norm] = 0.0 SFV[high] = 0.252 SDV[low] = 0.0SDV[norm] = 0.641 SDV[high] = 0.0 Step 2: Feed into Full Clog InferenceMachine CLOG[low] = 0.175 CLOG[norm] = 0.0 CLOG[high] = 0.063 FD(Centerof Maxes) = −11 < 20, therefore no full clog is inferred, Step 3: Feedinto Partial Clog Inference Machine CLOG[low ] = 0.0775 CLOG[norm] =0.411 CLOG[high] = 0.458 FD(Center of Maxes) = 34 > 25, thereforepartial clog is inferred,

An example is now described.

A. Test Setup

Three sets of data were tested. The 3 sets are GUNKY, LEARNING and TEST.As the name implies the GUNKY set represents samples that are likely tocause clogs in the flow cell. Amorphous specimens, specimens with lotsof crystals and specimens with lots of bacteria are examples of thesamples which comprise the GUNKY set. The GUNKY set does not representthe population. The LEARNING set, however, is a better representation ofthe population and more realistic samples. The LEARNING set was used infine-tuning the fuzzy logic. After fine-tuning the fuzz logic, it wastested on TEST set. The data are collected from two different flow cellsystems. When these samples were run, the operator visually observed theprocessing of each specimen and classified them into no clog, partialclog and full clog. The Fuzzy Logic was applied to these data sets. TheFuzzy Logic classification was compared to the Operator'sclassification.

Test Results

The following terms, defined below, are employed in describing the testand results.

True Positive (TP)

Operator reported Clog and Fuzzy Logic yielded Clog

True Negative (TN)

Operator reported No Clog and Fuzzy Logic yielded No Clog

False Positive (FP)

Operator reported No Clog but Fuzzy Logic yielded Clog

False Negative (FN)

Operator reported Clog but Fuzzy Logic yielded No Clog

Sensitivity

TP/(TP+FN)

Specificity

TN (FP+TN)

Positive Predictive Value (PPV)

TP/(TP+FP)

Negative Predictive Value (NPV)

TN/(TN+FN)

Efficiency

(TP+TN)/(TP+FP+FN+TN)

The test results for the Fuzzy Logic classifications are shown in tablesbelow:

TABLE 1 LEARNING Set Fuzzy: Fuzzy: Fuzzy: Full Clog Partial Clog No ClogOperator: Full Clog 11 0 0 Operator: Partial  1 2 6 Clog Operator: NoClog 11 8 414 

TABLE 2 LEARNING Set With Positive Full and Partial Clogs Added Fuzzy:Clog Fuzzy: No Clog Operator: Clog 14  6 Operator: No Clog 19 414

TABLE 3 TEST Set Fuzzy: Fuzzy: Fuzzy: Full Clog Partial Clog No ClogOperator: Full Clog 13 1 1 Operator: Partial  6 5 6 Clog Operator: NoClog 23 7 510 

TABLE 4 TEST Set With Positive Full and Partial Clogs Added Fuzzy: ClogFuzzy: No Clog Operator: Clog 25  7 Operator: No Clog 30 510

TABLE 5 GUNKY Set Fuzzy: Fuzzy: Fuzzy: Full Clog Partial Clog No ClogOperator: Full Clog 8 0 1 Operator: Partial 1 5 4 Clog Operator: No Clog0 3 31 

TABLE 6 GUNKY Set With Positive Full and Partial Clogs Added Fuzzy: ClogFuzzy: No Clog Operator: Clog 14  5 Operator: No Clog  3 31

TABLE 7 All Samples Fuzzy: Fuzzy: Fuzzy: Full Clog Partial Clog No ClogOperator: Full Clog 32  1  2 Operator: Partial  8 12 16 Clog Operator:No Clog 34 18 955 

TABLE 8 All Samples With Positive Full and Partial Clogs Added Fuzzy:Clog Fuzzy: No Clog Operator: Clog 53  18 Operator: No Clog 52 955

TABLE 9 Fuzzy Logic Performance Sensitivity Specificity PPV NPVEfficiency LEARNING 0.7 0.956 0.424 0.986 0.945 TEST 0.78 0.944 0.4550.986 0.935 GUNKY 0.77 0.912 0.824 0.861 0.849 ALL 0.747 0.948 0.5050.982 0.935 SAMPLES

It should be understood that various embodiments of the inventiondescribed hereinabove are applicable beyond the area of fluid flowthrough a flow cell. For example, it is important that particlescontained within a fluid on a microscope slide (e.g., presented as a wetmount or as a dry smear), to be examined microscopically, are uniformlydistributed throughout an examination area. For example, blood cells ina blood smear should be uniformly distributed over the examination areaof the microscope slide. In such an embodiment, fields of theexamination area are scanned (and examined in temporal succession) asdisclosed, for example, in U.S. Pat. No. 5,625,709 (incorporated hereinby reference in its entirety), the particles of these scanned fields arecounted, and the counts subjected to the statistical analysis set forthabove. In addition, in enhancements of this embodiment, it is determinedwhich particular sub-areas of the examination area have a uniformdistribution of particles, and the results of the particle analysis arethen based only on these particular sub-areas.

It should be further understood that, while in the described embodimentsmany of the method steps for determining flow quality are carried out bythe CPU 56 executing special purpose firmware or software, it is withinthe scope of the invention for some or all of the method steps to beperformed by hardwired circuitry.

What is claimed is:
 1. A method of determining flow quality of a fluid sample, in a particle analyzer, through an examination area of a flow chamber, comprising: providing a heavy stain as at least a portion of the fluid sample; determining, by the particle analyzer, if there is a period of time during which a stained portion of the fluid sample passes through the examination area after the providing step and is uniformly distributed within a first portion of the examination area, to generate a determination signal; and determining the flow quality of the fluid sample in response to the determination signal.
 2. The method of claim 1, wherein the first portion of the examination area is a portion of the examination area which spans the examination area in a direction perpendicular to a direction of flow of the fluid sample through the examination area.
 3. The method of claim 1, wherein the providing step is executed by the particle analyzer concurrently with the particle analyzer analyzing particles in a fluid sample.
 4. The method of claim 1, wherein the providing step is executed by the particle analyzer at a time when the particle analyzer is not analyzing particles in a fluid sample.
 5. The method of claim 1, wherein the particle analyzer includes a light source illuminating the examination area from a first side and an imaging source imaging the examination area from a second side, opposite the first side, to generate a plurality of images, and wherein, in the determining step, the particle analyzer determines from the images if there is a period of time during which a stained portion of the fluid sample passes through the examination area after the providing step and is uniformly distributed within the first portion of the examination area.
 6. The method of claim 5, wherein the first portion of the examination area is a portion of the examination area which spans the examination area in a direction perpendicular to a direction of flow of the fluid sample through the examination area.
 7. The method of claim 1, wherein the at least one parameter includes a weighted sum of the particle counts. 